Leptospirosis outbreaks in various parts of the world have been linked to changes in the weather. Furthermore, the effects have been shown to occur at different lags of up to 10 months, affecting the performance of simulation models that predict leptospirosis occurrence. In Malaysia, the link between different weather parameters, at different time lags, has yet to be established despite an increasing number of cases in recent years. In this study, a combination of data mining and machine learning is used to analyze, capture, and predict the relation between leptospirosis occurrence and temperature, rainfall, and relative humidity using the Seremban district in Malaysia as a case study. First, the optimal time lags for rainfall were determined using graphical exploratory data analysis (EDA) while non-graphical EDA was used for temperature. Then, an artificial neural network (ANN) model is developed to classify the combination of selected features into disease occurrence and non-occurrence using back-propagation training, optimizing the number of hidden layers and hidden nodes. The success is measured using accuracy, sensitivity, and specificity of each model. EDA has shown that leptospirosis occurrence in Seremban is highly correlated with weekly average temperature at lag 16 weeks and weekly rainfall amount at lag 12-20 weeks. Using these selected features, the ANN model achieved the highest accuracy, sensitivity, and specificity at 84.00, 86.44, and 79.33%, respectively. Overall, the EDA approach has increased the accuracy of the predictive model by 13.30-31.26% from the baseline models.
Introduction. Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method. All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10 ′ N, longitude 102°18 ′ E) was analysed with dengue data. Result. A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion. Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.
Objectives: Dengue remains hyperendemic in Malaysia despite extensive vector control activities. With dynamic changes in land use, urbanisation and population movement, periodic updates on dengue transmission patterns are crucial to ensure the implementation of effective control strategies. We sought to assess shifts in the trends and spatial patterns of dengue in Kelantan, a north-eastern state of Malaysia (5°15’N 102°0’E).Methods: This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering.Results: A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts.Conclusions: A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.
Waterborne disease has a worldwide distribution and it was frequently happening in developing countries but rarely happen in developed countries [1]. The waterborne disease belongs to the top five common diseases that cause of death. While leptospirosis is one of the top killers of water-borne diseases because more than 500 000 cases were recorded every year [2]. Malaysia is one of the developing countries and is one of the countries that face this disaster [3]. Generally, human will be infected with leptospirosis when they have direct contacts with the product of infected animals such as urine. Besides, they also can be infected by the indirect way by contact with the contaminated water or soil which consist of the leptospira species. This disease also can be infected through human to human transmission but based on the number of cases it is very rare [4]. This pathogen can survive in tropical and subtropical environments Abstract: Leptospirosis is one of the waters borne diseases that widespread in Asia Pacific regions, especially developed countries. Over the past few years, the clinical data have shown Seremban experienced a significant number of leptospirosis patient. To minimize the impact of this disease, this study has set one objective which is to develop one prediction model to predict the leptospirosis diseases confirmed-cases by using Back-Propagation Neural Network (BPNN). A growing number of studies has shown the climate can be a predictor in outbreak incidence. Likewise, climate variable such as rainfall, temperature, and relative humidity affect in many ways especially for the transmission of vector and pathogens. Thus, these 3 parameters will be the main input for this model. Technically, this study will focus on the accuracy and the sensitivity of the model by finding the relationship between the meteorological data and clinical data. The clinical data was provided from the ministry of health Negeri Sembilan, while the meteorological data was provided from the Drainage and Irrigation Department and the Malaysian Meteorological Department. This study acknowledges that the amount of rainfall was correlated with the leptospirosis cases in all region of Seremban states such as Mantin, Seremban, Perentian, and Sikamat. In this study, preliminary exploration was performed by finding the best time for the meteorological data to correlate with clinical data (1 until 5-month lag). The model achieved 70% accuracy in prediction by combining the sum of rainfall, relative humidity, and temperature with 3-month lag as an input of the BPNN model. In conclusion, the authors believe this achievement of the model is an early stage for the prediction model. This model can achieve more than 70% accuracy by adapting some exploratory data analysis for every single variable or predictor.
The COVID-19 pandemic has hastened the progress of digitalization where the public is forced to embrace paradigm shifts on how we function in a digital society. The way we work, learn, live, and play daily has drastically changed with the revolution of digital systems from their analog predecessor. This transformation warrants the digital environment as a social determinant of health. It comprises the whole continuum from the tangible aspects of the computing devices, their programing and information system, the network technologies connecting them, and the product of interactivity between people to people and people to the digital interface. Despite permeating the everyday life of each level of society, the digital environment has yet to be scrutinized comprehensively in terms of health. A review of the literature produces fragmented results where different specialties within and beyond the medical field lay claim to the various aspects of digitalization. We proposed five domains within the digital environment namely digital transformation, digital health, digital technology, digital identity, and digital media that exerts diversified pressure on the digital environment through human activities. Their subjacent linkage to human health and environmental impact is further discussed by using the DPSEEA framework. Challenges that crossed all domains were discussed including the widening gap of inequalities secondary to the limited availability of, and accessibility to digitalization. Considering the rapid speed at which we propel to a fully immersive virtual world, a timely transformation of environmental health to include the digital environment as part of its main components is inevitable.
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